Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities

TL;DR

This paper presents a deep learning-based approach for detecting energy anomalies in sports facilities, significantly improving accuracy and reliability over traditional methods, thereby aiding energy management and operational efficiency.

Contribution

The study introduces a novel deep learning framework with threshold estimation and false alarm reduction for anomaly detection in sports facility energy data.

Findings

Achieved 94.33% accuracy in anomaly detection.

Attained 92.92% F1-score, outperforming conventional techniques.

Validated on aquatic center dataset at Qatar University.

Abstract

Anomaly detection in sport facilities has gained significant attention due to its potential to promote energy saving and optimizing operational efficiency. In this research article, we investigate the role of machine learning, particularly deep learning, in anomaly detection for sport facilities. We explore the challenges and perspectives of utilizing deep learning methods for this task, aiming to address the drawbacks and limitations of conventional approaches. Our proposed approach involves feature extraction from the data collected in sport facilities. We present a problem formulation using Deep Feedforward Neural Networks (DFNN) and introduce threshold estimation techniques to identify anomalies effectively. Furthermore, we propose methods to reduce false alarms, ensuring the reliability and accuracy of anomaly detection. To evaluate the effectiveness of our approach, we conduct experiments on aquatic center dataset at Qatar University. The results demonstrate the superiority of our deep learning-based method over conventional techniques, highlighting its potential in real-world applications. Typically, 94.33% accuracy and 92.92% F1-score have been achieved using the proposed scheme.